Citation

Vlachogiannis DM, Vlahogianni EI, Golias J. Int. J. Transp. Sci. Technol. 2020; 9(4): 299-308.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.ijtst.2020.03.002

PMID

unavailable

Abstract

Optimizing driving performance by addressing personalized aspects of driving behavior and without posing unrealistic restrictions on personal mobility may have far reaching implications to traffic safety, flow operations and the environment, as well as significant benefits for users. The present work addresses the problem of delivering personalized driving policies based on Reinforcement Learning for enhancing existing Intelligent Transportation Systems (ITS) to the benefit of traffic management and road safety. The proposed framework is implemented on appropriate driving behavior metrics derived from smartphone sensors' data streams. Aggressiveness, speeding and mobile usage are considered to describe the driving profile per trip and are presented as inputs to the Q-learning algorithm. The implementation of the proposed methodological approach produces personalized quantified driving policies to be exploited for self-improvement. Finally, this paper establishes validation measures of the quality and effectiveness of the produced policies and methodological tools for comparing and classifying the examined drivers.

Language: en

Keywords

Intelligent transportation systems; Machine learning; Q-learning; Reinforcement learning; Traffic data